scholarly journals Video-Based Contactless Heart-Rate Detection and Counting via Joint Blind Source Separation with Adaptive Noise Canceller

2019 ◽  
Vol 9 (20) ◽  
pp. 4349 ◽  
Author(s):  
Kanghyu Lee ◽  
Junmuk Lee ◽  
Changwoo Ha ◽  
Minseok Han ◽  
Hanseok Ko

Driver assistance systems are a major focus of the automotive industry. Although technological functions that help drivers are improving, the monitoring of driver state functions receives less attention. In this respect, the human heart rate (HR) is one of the most important bio-signals, and it can be detected remotely using consumer-grade cameras. Based on this, a video-based driver state monitoring system using HR signals is proposed in this paper. In a practical automotive environment, monitoring the HR is very challenging due to changes in illumination, vibrations, and human motion. In order to overcome these problems, source separation strategies were employed using joint blind source separation, and feature combination was adopted to maximize HR variation. Noise-assisted data analysis was then adopted using ensemble empirical mode decomposition to extract the pure HR. Finally, power spectral density analysis was conducted in the frequency domain, and a post-processing smoothing filter was applied. The performance of the proposed approach was tested based on commonly employed metrics using the MAHNOB-HCI public dataset and compared with recently proposed competing methods. The experimental results proved that our method is robust for a variety of driving conditions based on testing using a driving dataset and static indoor environments.

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kanghyu Lee ◽  
David K. Han ◽  
Hanseok Ko

We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.


2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Boyuan Zhang ◽  
Hengkang Li ◽  
Lisheng Xu ◽  
Lin Qi ◽  
Yudong Yao ◽  
...  

Remote photoplethysmography (rPPG) can be used for noncontact and continuous measurement of the heart rate (HR). Currently, the main factors affecting the accuracy and robustness of rPPG-based HR measurement methods are the subject’s skin tone, body movement, exercise recovery, and variable or inadequate illumination. In response to these challenges, this study is aimed at investigating a rPPG-based HR measurement method that is effective under a wide range of conditions by only using a webcam. We propose a new approach, which combines joint blind source separation (JBSS) and a projection process based on a skin reflection model, so as to eliminate the interference of background illumination and enhance the extraction of pulse rate information. Three datasets derived from subjects with different skin tones considering six environmental scenarios are used to validate the proposed method against three other state-of-the-art methods. The results show that the proposed method can provide more accurate and robust HR measurement for all three datasets and is therefore more applicable to a wide range of scenarios.


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